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» Evaluating algorithms that learn from data streams
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ACL
2007
15 years 8 months ago
Fast Unsupervised Incremental Parsing
This paper describes an incremental parser and an unsupervised learning algorithm for inducing this parser from plain text. The parser uses a representation for syntactic structur...
Yoav Seginer
BMCBI
2010
182views more  BMCBI 2010»
15 years 6 months ago
L2-norm multiple kernel learning and its application to biomedical data fusion
Background: This paper introduces the notion of optimizing different norms in the dual problem of support vector machines with multiple kernels. The selection of norms yields diff...
Shi Yu, Tillmann Falck, Anneleen Daemen, Lé...
SECON
2008
IEEE
16 years 29 days ago
Enhancing the Data Collection Rate of Tree-Based Aggregation in Wireless Sensor Networks
— What is the fastest rate at which we can collect a stream of aggregated data from a set of wireless sensors organized as a tree? We explore a hierarchy of techniques using real...
Özlem Durmaz Incel, Bhaskar Krishnamachari
BMCBI
2010
160views more  BMCBI 2010»
15 years 6 months ago
Annotation of gene promoters by integrative data-mining of ChIP-seq Pol-II enrichment data
Background: Use of alternative gene promoters that drive widespread cell-type, tissue-type or developmental gene regulation in mammalian genomes is a common phenomenon. Chromatin ...
Ravi Gupta, Priyankara Wikramasinghe, Anirban Bhat...
ALGORITHMICA
2006
74views more  ALGORITHMICA 2006»
15 years 6 months ago
Parallelizing Feature Selection
Classification is a key problem in machine learning/data mining. Algorithms for classification have the ability to predict the class of a new instance after having been trained on...
Jerffeson Teixeira de Souza, Stan Matwin, Nathalie...